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The proposed research aims to address the critical need for robust disease detection in tomato crops in the Middle East, given the essential role of tomatoes in the region's food security. Recognizing the significance of plant disease detection becomes important for securing a stable supply of this essential crop. This research aims to employ transfer learning and A modified InceptionResNetV2 architecture for the purpose of disease detection. The study evaluates the efficiency of this model with two datasets: Plant Village and Noulam. The Proposed Model Surpasses Prior Works: Achieving 99.8% and 99.7% Accuracies on Respective Datasets for Tomato Disease Detection. The primary objective of this research is to enhance the early detection of tomato diseases, contributing to the efficiency and sustainability of tomato cultivation.

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Enhancing Tomato Crop Health: Leveraging Modified InceptionResNetV2 for Disease Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Fida Zubair,
  • Moutaz Saleh

摘要

The proposed research aims to address the critical need for robust disease detection in tomato crops in the Middle East, given the essential role of tomatoes in the region's food security. Recognizing the significance of plant disease detection becomes important for securing a stable supply of this essential crop. This research aims to employ transfer learning and A modified InceptionResNetV2 architecture for the purpose of disease detection. The study evaluates the efficiency of this model with two datasets: Plant Village and Noulam. The Proposed Model Surpasses Prior Works: Achieving 99.8% and 99.7% Accuracies on Respective Datasets for Tomato Disease Detection. The primary objective of this research is to enhance the early detection of tomato diseases, contributing to the efficiency and sustainability of tomato cultivation.